@inproceedings{44326,
  abstract     = {{Low-quality models that miss relevant dynamics lead to major challenges in modelbased
state estimation. We address this issue by simultaneously estimating the system’s states
and its model inaccuracies by a square root unscented Kalman filter (SRUKF). Concretely,
we augment the state with the parameter vector of a linear combination containing suitable
functions that approximate the lacking dynamics. Presuming that only a few dynamical terms
are relevant, the parameter vector is claimed to be sparse. In Bayesian setting, properties like
sparsity are expressed by a prior distribution. One common choice for sparsity is a Laplace
distribution. However, due to disadvantages of a Laplacian prior in regards to the SRUKF,
the regularized horseshoe distribution, a Gaussian that approximately features sparsity, is
applied instead. Results exhibit small estimation errors with model improvements detected by
an automated model reduction technique.}},
  author       = {{Götte, Ricarda-Samantha and Timmermann, Julia}},
  booktitle    = {{IFAC-PapersOnLine}},
  keywords     = {{joint estimation, unscented Kalman filter, sparsity, Laplacian prior, regularized horseshoe, principal component analysis}},
  location     = {{Yokohama, Japan}},
  number       = {{2}},
  pages        = {{869--874}},
  title        = {{{Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF}}},
  volume       = {{56}},
  year         = {{2023}},
}